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Genomic structure and marker-derived gene networks for growth and meat quality traits of Brazilian Nelore beef cattle

Mudadu, Maurício A.; Porto-Neto, Laercio R.; Mokry, Fabiana B.; Tizioto, Polyana C.; Oliveira, Priscila S. N.; Tullio, Rymer R.; Nassu, Renata T.; Niciura, Simone C. M.; Tholon, Patrícia; Alencar, Maurício M.; Higa, Roberto H.; Rosa, Antônio N.; Feijó, Gélson L. D.; Ferraz, André L. J.; Silva, Luiz O. C.; Medeiros, Sérgio R.; Lanna, Dante P.; Nascimento, Michele L.; Chaves, Amália S.; Souza, Andrea R. D. L.; Packer, Irineu U.; Torres Junior, Roberto A. A.; Siqueira, Fabiane; Mourão, Gerson B.; Coutinho, Luiz L.; Reverter, Antonio; Regitano, Luciana C. A. Universidade De São Paulo

BMC Genomics

BioMed Central 2016

Acesso online

  • Título:
    Genomic structure and marker-derived gene networks for growth and meat quality traits of Brazilian Nelore beef cattle
  • Autor: Mudadu, Maurício A.; Porto-Neto, Laercio R.; Mokry, Fabiana B.; Tizioto, Polyana C.; Oliveira, Priscila S. N.; Tullio, Rymer R.; Nassu, Renata T.; Niciura, Simone C. M.; Tholon, Patrícia; Alencar, Maurício M.; Higa, Roberto H.; Rosa, Antônio N.; Feijó, Gélson L. D.; Ferraz, André L. J.; Silva, Luiz O. C.; Medeiros, Sérgio R.; Lanna, Dante P.; Nascimento, Michele L.; Chaves, Amália S.; Souza, Andrea R. D. L.; Packer, Irineu U.; Torres Junior, Roberto A. A.; Siqueira, Fabiane; Mourão, Gerson B.; Coutinho, Luiz L.; Reverter, Antonio; Regitano, Luciana C. A.
  • Universidade De São Paulo
  • Assuntos: Genotyping; Awm; Pcit; Gwas
  • É parte de: BMC Genomics
  • Descrição: Abstract Background Nelore is the major beef cattle breed in Brazil with more than 130 million heads. Genome-wide association studies (GWAS) are often used to associate markers and genomic regions to growth and meat quality traits that can be used to assist selection programs. An alternative methodology to traditional GWAS that involves the construction of gene network interactions, derived from results of several GWAS is the AWM (Association Weight Matrices)/PCIT (Partial Correlation and Information Theory). With the aim of evaluating the genetic architecture of Brazilian Nelore cattle, we used high-density SNP genotyping data (~770,000 SNP) from 780 Nelore animals comprising 34 half-sibling families derived from highly disseminated and unrelated sires from across Brazil. The AWM/PCIT methodology was employed to evaluate the genes that participate in a series of eight phenotypes related to growth and meat quality obtained from this Nelore sample. Results Our results indicate a lack of structuring between the individuals studied since principal component analyses were not able to differentiate families by its sires or by its ancestral lineages. The application of the AWM/PCIT methodology revealed a trio of transcription factors (comprising VDR, LHX9 and ZEB1) which in combination connected 66 genes through 359 edges and whose biological functions were inspected, some revealing to participate in biological growth processes in literature searches. Conclusions The diversity of the Nelore sample studied is not high enough to differentiate among families neither by sires nor by using the available ancestral lineage information. The gene networks constructed from the AWM/PCIT methodology were a useful alternative in characterizing genes and gene networks that were allegedly influential in growth and meat quality traits in Nelore cattle.
    This study was conducted with funding from EMBRAPA (Macroprograma1, 01/2005) and FAPESP (process number 2012/23638-8). GBM, LLC, LCAR and MMA were granted CNPq fellowships. We thank Sean McWilliam, Marina R. S. Fortes, Edilson Guimaraes, Robson Rodrigues Santiago, Roselito F. da Silva, Fernando F. Cardoso, Flavia Aline Bressani, Wilson Malago Jr., Avelardo U. C. Ferreira, Michel E. B. Yamaguishi and Fabio D. Vieira for the help and technical assistance. The authors would like to acknowledge the collaborative efforts among EMBRAPA, University of Sao Paulo and CSIRO.
  • DOI: 10.1186/s12864-016-2535-3
  • Títulos relacionados: BMC Genomics
  • Editor: BioMed Central
  • Data de publicação: 2016
  • Idioma: Inglês

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